Unit 4, 6 Sampling Example knowitvideos English Portuguese

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Here's a new network that we'll use to investigate

Aqui está uma nova rede que usaremos para investigar

how sampling can be used to do inference.

como a amostragem pode ser utilizada para fazer inferência.

In this network, we have 4 variables. They're all boolean.

Nessa rede, temos 4 variáveis. São todas booleanas.

Cloudy tells us if it's cloudy or not outside,

Nublado diz se do lado de fora está nublado ou não,

and that can have an effect on whether the sprinklers are turned on,

e isso pode influenciar se os irrigadores estão ligados,

and whether it's raining.

e também se está chovendo.

And those 2 variables in turn have an effect on whether the grass gets wet.

Essas duas variáveis, por sua vez, influenciam se a grama ficou molhada.

Now, to do inference over this network using sampling,

Para realizar inferência sobre essa rede usando amostragem,

we start off with a variable where all the parents are defined.

começamos com uma variável em que todos os pais já estão definidos.

In this case, there's only one such variable, Cloudy.

Nesse caso, só há uma variável, Nublado.

And it's conditional probability table tells us that the probability is 50% for Cloudy,

A tabela de probabilidade condicional diz que há 50% de chance de estar nublado,

50% for not Cloudy, and so we sample from that.

e 50% de não estar, então fazemos a amostragem disso.

We generate a random number, and let's say it comes up with positive for Cloudy.

Geramos um número aleatório, e digamos que ele dá positivo para Nublado.

Now that variable is defined, we can choose another variable.

Agora que a variável está definida, podemos escolher outra.

In this case, let's choose Sprinkler, and we look at the rows in the table

Vamos escolher Irrigador, então olhamos as linhas da tabela

for which Cloudy, the parent, is positive, and we see we should sample

para o qual Nublado, seu pai, é positivo, então vemos que devemos fazer a amostragem

with probability 10% to +s and 90% a -s.

com probabilidade de 10% para +s e 90% para -s.

And so let's say we do that sampling with a random number generator,

Digamos que fizemos a amostragem com um gerador de números aleatórios,

and it comes up negative for Sprinkler.

e Irrigador dá negativo.

Now let's jump over here. Look at the Rain variable.

Vamos pular pra cá. Veja a variável Chuva.

Again, the parent, Cloudy, is positive,

Como antes, seu pai, Nublado, é positivo,

so we're looking at this part of the table.

então verificamos essa parte da tabela.

We get a 0.8 probability for Rain being positive,

Temos probabilidade de 0.8 para a Chuva dar positivo,

and a 0.2 probability for Rain being negative.

e 0.2 para ela dar negativo.

Let's say we sample that randomly, and it comes up Rain is positive.

Vamos dizer que fizemos a amostragem aleatoriamente, e Chuva dá positivo.

And now we're ready to sample the final variable,

Agora sim podemos partir para a última variável,

and what I want you to do is tell me which of the rows

e o que eu quero que você faça é me responder quais linhas

of this table should we be considering and tell me what's more likely.

dessa tabela deveríamos considerar. Diga-me ainda qual resultado é mais provável.

Is it more likely that we have a +w or a -w?

É mais provável dar +w ou -w?

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